library(ClusterR)
## Loading required package: gtools
library(knitr)
library(rgl)
library(rglwidget)
## The functions in the rglwidget package have been moved to rgl.
knit_hooks$set(webgl = hook_webgl)
library(scatterplot3d)
library(ggplot2)
library(sm)
## Package 'sm', version 2.2-5.4: type help(sm) for summary information
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
clustME <- function(numberOfClusters,clust,loc_gc) {
cols <- paste0("clust",c(1:numberOfClusters))
clusStats = data.frame(K=numeric(),WithinSS=numeric())
i=1
for(i in 1:numberOfClusters){
print(i)
km_rc = KMeans_rcpp(clust, clusters = i, num_init = 100, max_iters = 1000,
initializer = 'optimal_init', threads = 4, verbose = T)
tmp=data.frame(km_rc$clusters)
colnames(tmp)=c(cols[i])
loc_gc=cbind(loc_gc,tmp)
print(colnames(loc_gc))
tmp2 = data.frame(K=c(i),WithinSS=c(mean(km_rc$between.SS_DIV_total.SS)))
clusStats= rbind(clusStats,tmp2)
}
save(clusStats,file = "clusStats.RData")
return(loc_gc)
}
















































##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
## The following object is masked from 'package:sm':
##
## muscle
